Analysis of Text Mining from Full-text Articles and Abstracts by Postgraduates Students in Selected Nigeria Universities
Bibliographic record
Abstract
Purpose: This study analysed text mining from full-text articles and abstracts by postgraduate students in selected Nigeria universities.Design/methodology/approach: The study adopted a survey research design using a questionnaire as the instrument for data collection from 357 postgraduate students drawn using Raosoft sample size calculator. Six research questions were developed and answered.Finding: The findings demonstrate that postgraduate students mined texts from full texts articles mostly to write a dissertation, for personal academic development and to prepare research seminars. It also revealed that postgraduate students mined texts from abstracts majorly to write dissertations and prepare for research seminars; postgraduate students mined texts using information extraction technique, information retrieval technique, and summarization. The texts are mined mostly form PDF format, followed by Microsoft word format and HTML format (Web pages). Postgraduate students prefer mining texts from full-text articles than from abstracts and the sources postgraduate students mostly mine text is through the World Wide Web, followed by library databases.Research limitations/implications: The current study only used a questionnaire, a self-reported survey to collect data from the respondents of the study. Including other data collection instruments such as interviews would provide a holistic view of the data mining scenario from both the full-text articles and abstracts among the postgraduate students in Nigerian universities and this would make the generalisation of the study findings easier and more worthwhile.Originality/value: Research on data mining either from full-text articles or abstracts were predominantly conducted in Advance countries. This study seems to be one of the pioneer studies in this area in Nigeria and Africa as a whole. It is the original idea by the author; and it is assumed that understanding the nature and context-related information in data mining by the postgraduate students is an original idea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".